NeuroADDA: Active Discriminative Domain Adaptation in Connectomic
Training segmentation models from scratch has been the standard approach for new electron microscopy connectomics datasets. However, leveraging pretrained models from existing datasets could improve efficiency and performance in constrained annotation budget. In this study, we investigate domain adaptation in connectomics by analyzing six major datasets spanning different organisms. We show that, Maximum Mean Discrepancy (MMD) between neuron image distributions serves as a reliable indicator of transferability, and identifies the optimal source domain for transfer learning. Building on this, we introduce NeuroADDA, a method that combines optimal domain selection with source-free active learning to effectively adapt pretrained backbones to a new dataset. NeuroADDA consistently outperforms training from scratch across diverse datasets and fine-tuning sample sizes, with the largest gain observed at $n=4$ samples with a 25-67\% reduction in Variation of Information. Finally, we show that our analysis of distributional differences among neuron images from multiple species in a learned feature space reveals that these domain "distances" correlate with phylogenetic distance among those species.
Code (0)
등록된 구현이 없습니다.
Tasks
Active LearningDomain AdaptationTransfer LearningSimilar Papers 제목 키워드 기반
An Out-of-Domain Synapse Detection Challenge for Microwasp Brain Connectomes
The size of image stacks in connectomics studies now reaches the terabyte and often petabyte scales with a great diversity of appearance across brain regions and samples. However, manual annotation of neural structures, …
DiversityDomain AdaptationDiscriminative Active Learning for Domain Adaptation
Domain Adaptation aiming to learn a transferable feature between different but related domains has been well investigated and has shown excellent empirical performances. Previous works mainly focused on matching the marg…
Active LearningDiversityDomain AdaptationActive Adversarial Domain Adaptation
We propose an active learning approach for transferring representations across domains. Our approach, active adversarial domain adaptation (AADA), explores a duality between two related problems: adversarial domain align…
Active LearningDiversityDomain Adaptationobject-detection+2An In-Depth Analysis of Adversarial Discriminative Domain Adaptation for Digit Classification
Domain adaptation is an active area of research driven by the growing demand for robust machine learning models that perform well on real-world data. Adversarial learning for deep neural networks (DNNs) has emerged as a …
ClassificationDomain Adaptationdomain classificationimage-classification+1Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification
Recent years have witnessed the quick progress of the hyperspectral images (HSI) classification. Most of existing studies either heavily rely on the expensive label information using the supervised learning or can hardly…
Active LearningClassificationDomain AdaptationGeneral Classification+3